Information Science Students' Background and Data Science Competencies: An Exploratory Study

Autor/inn/en	Rosenfeld, Ariel; Elmalech, Avshalom
Titel	Information Science Students' Background and Data Science Competencies: An Exploratory Study
Quelle	In: Journal of Education for Library and Information Science, 64 (2023) 4, S.385-403 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0748-5786
DOI	10.3138/jelis-2021-0076
Schlagwörter	Graduate Students; Information Science; Data Science; Competence; Background; Artificial Intelligence; Grade Point Average; Educational Quality; Predictor Variables + Suchen Sie Ihr Suchwort? Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin; Informationswissenschaft; Kompetenz; Hintergrundinformation; Künstliche Intelligenz; Quality of education; Bildungsqualität; Prädiktor
Abstract	Many Library and Information Science (LIS) training programs are gradually expanding their curricula to include computational data science courses such as supervised and unsupervised machine learning. These programs focus on developing both "classic" information science competencies as well as core data science competencies among their students. Since data science competencies are often associated with mathematical and computational thinking, departmental officials and prospective students often raise concerns regarding the appropriate background students should have in order to succeed in this newly introduced computational content of the LIS training programs. In order to address these concerns, we report on an exploratory study through which we examined the 2020 and 2021 student classes of Bar-Ilan University's LIS graduate training, focusing on the computational data science courses (i.e., supervised and unsupervised machine learning). Our study shows that contrary to many of the concerns raised, students from the humanities performed as well (and in some cases significantly better) on data science competencies compared to those from the social sciences and had better success in the training program as a whole. In addition, students' undergraduate GPA acted as an adequate indicator for both their success in the training program and in the data science part thereof. In addition, we find no evidence to support concerns regarding age or sex. Finally, our study suggests that the computational data science part of students' training is very much aligned with the rest of their training program. (As Provided).
Anmerkungen	Association for Library and Information Science Education. Available from: University of Toronto Press. 5201 Dufferin Street, Toronto, ON, M3H 5T8 Canada. Tel: 416-667–7929; Fax: 416-667–7832; e-mail: journals@utpress.utoronto.ca; e-mail: office@alise.org; Web site: https://www.utpjournals.press/loi/jelis
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01